Exploring the Core Factors and Forecasting the Energy Consumption in China's Transport Sector

Abstract

Abstract:

With the acceleration of industrialization and urbanization, transportation has become the second largest (around 10%-20% of total energy demand) energy intensive sector in China, next only to industry. In this context, it is of great necessity to figure out the main factors that affect energy consumption in China's transport sector, and make reasonable prediction. Firstly, this paper builds a Bayesian structural equation model (BSEM) to describe the influencing mechanism from three aspects:economic aggregate, technical progress and transportation structure. Next, path-analysis technology is applied to extract the main factors, including transportation converted turnover, per capita disposable income of urban households, energy consumption per unit of turnover, the ratio of transport fixed asset investment and the ratio of road and civil aviation turnover. Based on this analysis, VAR model is built to explore the dynamic time-delay relationship between energy consumption in transport sector and the main factors. The impulse response analysis indicates that when imposing a positive impulse on transport turnover, the significant motivating effect for transportation energy consumption lasts for a long period; the response of transport energy consumption to energy consumption per unit turnover are distinct periodically-in the short term, it will cause the increase of traffic energy consumption due to "rebound effect", but in the long-term, it inhibits the increase of energy consumption due to technological progress effect; the ratio of highway and civil aviation turnover also has a positive impact on transportation energy consumption. Finally BVAR model is constructed on the basis of Gibbs sampling to predict traffic energy consumption from 2015 to 2020, which respectively is 433.93, 470.76, 510.85, 554.52, 602.09, 653.92 million tons of standard coal. The fitting results show that the relative prediction error is 2.87%, implying the predicted results are pretty reliable. Lastly, this paper puts forward related suggestions about energy saving in China's transport sector.

Key words: transport energy consumption, Bayesian structural equation model, path-analysis, VAR, BVAR

Chai Jian, Xing Limin, Lu Quanyin, Hu Yi, Wang Shouyang. Exploring the Core Factors and Forecasting the Energy Consumption in China's Transport Sector[J]. , 2018, 30(3): 201-214.

References

[1] Zhang M., Li H. N., Zhou M., et al. Decomposition Analysis of Energy Consumption in Chinese Transportation Sector[J]. Applied Energy, 2011,88(6):2278-2285
[2] Jiang J. A Factor Decomposition Analysis of Transportation Energy Consumption and Related Policy Implications[J]. IATSS Research, 2015,38(2):142-148
[3] Chung W., Zhou G., Yeung I. M. H. A Study of Energy Efficiency of Transport Sector in China from 2003 to 2009[J]. Applied Energy, 2013,112(16):1066-1077
[4] Tiwari P., Gulati M. An Analysis of Trends in Passenger and Freight Transport Energy Consumption in India[J]. Research in Transportation Economics, 2013,38(1):84-90
[5] Wu H. M., Xu W. Cargo Transport Energy Consumption Factors Analysis:Based on LMDI Decomposition Technique[J]. IERI Procedia, 2014,9(9):168-175
[6] Azlina A. A., Law S. H., Mustapha N. H. N. Dynamic Linkages among Transport Energy Consumption, Income and CO₂ Emission in Malaysia[J]. Energy Policy, 2014,73(10):598-606
[7] Xu B., Lin B. Factors Affecting Carbon Dioxide (CO₂) Emissions in China's Transport Sector:A Dynamic Nonparametric Additive Regression Model[J]. Journal of Cleaner Production, 2015,101(15):311-322
[8] Chandran V. G. R., Tang C. F. The Impacts of Transport Energy Consumption, Foreign Direct Investment and Income on CO₂ Emissions in ASEAN-5 Economies[J]. Renewable and Sustainable Energy Reviews, 2013,24(10):445-453
[9] De Haan P., Peters A., Scholz R. W. Reducing Energy Consumption in Road Transport through Hybrid Vehicles:Investigation of Rebound Effects, and Possible Effects of Tax Rebates[J]. Journal of Cleaner Production, 2007,15(11):1076-1084
[10] Anson S., Turner K. Rebound and Disinvestment Effects in Refined Oil Consumption and Supply Resulting From an Increase in Energy Efficiency in the Scottish Commercial Transport Sector[J]. Energy Policy, 2009,37(9):3608-3620
[11] Chen Y., Fan Y. Coping with Technology Uncertainty in Transportation Fuel Portfolio Design[J]. Transportation Research Part D:Transport & Environment, 2014,32(5):354-361
[12] Janic'm M. Greening Commercial Air Transportation by Using Liquid Hydrogen (LH₂) as a Fuel[J]. International Journal of Hydrogen Energy, 2014,39(29):16426-16441
[13] Kim J., Moon I. The Role of Hydrogen in the Road Transportation Sector for a Sustainable Energy System:A Case Study of Korea[J]. International Journal of Hydrogen Energy, 2008,33(24):7326-7337
[14] Ahanchian M., Biona J. B. M. Energy Demand, Emissions Forecasts and Mitigation Strategies Modeled over a Medium-Range Horizon:The Case of the Land Transportation Sector in Metro Manila[J]. Energy Policy, 2014,66(1):615-629
[15] Hofer C., Sner M. E., Windle R. J. The Environmental Effects of Airline Carbon Emissions Taxation in the US[J]. Transportation Research Part D:Transport and Environment, 2010,15(1):37-45
[16] Chunark P., Promjiraprawat K., Limmeechokchai B. Impacts of CO₂ Reduction Target and Taxation on Thailand's Power System Planning Towards 2030[J]. Energy Procedia, 2014,52:85-92
[17] Fu M., Kelly J. A. Carbon Related Taxation Policies for Road Transport:Efficacy of Ownership and Usage Taxes, and the Role of Public Transport and Motorist Cost Perception on Policy Outcomes[J]. Transport Policy, 2012,22(4):57-69
[18] Cohen-Blankshtain G. Framing Transport-Environmental Policy:The Case of Company Car Taxation in Israel[J]. Transportation Research Part D:Transport and Environment, 2008,13(2):65-74
[19] Kunert U., Kuhfeld H. The Diverse Structures of Passenger Car Taxation in Europe and the EU Commissions Proposal for Reform[J]. Transport Policy, 2007,14(4):306-316
[20] 魏庆琦,赵嵩正,肖伟. 资源环境税对交通运输碳排放强度调节效应的实证研究[J]. 软科学, 2013,27(6):33-38
[21] Yin X., Chen W., Eom J., et al. China's Transportation Energy Consumption and CO₂ Emissions from a Global Perspective[J]. Energy Policy, 2015,82(1):233-248
[22] Baptista P. C., Silva C. M., Farias T. L., et al. Energy and Environmental Impacts of Alternative Pathways for the Portuguese Road Transportation Sector[J]. Energy Policy, 2012,51(4):802-815
[23] Bueno G. Analysis of Scenarios for the Reduction of Energy Consumption and GHG Emissions in Transport in the Basque Country[J]. Renewable and Sustainable Energy Reviews, 2012,16(4):1988-1998
[24] Pérez-Martínez P. J., Miranda R. M. Energy Consumption and Intensity of Toll Highway Transport in Spain[J]. Transportation Research Part D:Transport and Environment, 2014,27(1):1-5
[25] Hao H., Geng Y., Li W., et al. Energy Consumption and GHG Emissions from China's Freight Transport Sector:Scenarios through 2050[J]. Energy Policy, 2015,85(10):94-101
[26] Ahanchian M., Biona J. B. M. Energy Demand, Emissions Forecasts and Mitigation Strategies Modeled over a Medium-Range Horizon:The Case of the Land Transportation Sector in Metro Manila[J]. Energy Policy, 2014,66(1):615-629
[27] Antanasijevic' D., Pocajt V., Ristic' M., et al. Modeling of Energy Consumption and Related GHG (Greenhouse Gas) Intensity and Emissions in Europe Using General Regression Neural Networks[J]. Energy, 2015,84(5):816-824
[28] Cheng Y. H., Chang Y. H., Lu I. J. Urban Transportation Energy and Carbon Dioxide Emission Reduction Strategies[J]. Applied Energy, 2015,157(11):953-973
[29] Dua P., Ray S. C. A BVAR Model for the Connecticut Economy[J]. Journal of Forecasting, 1995,14(3):167-180
[30] Sarantis N., Stewart C. Structural, VAR and BVAR Models of Exchange Rate Determination:A Comparison of Their Forecasting Performance[J]. Journal of Forecasting, 1995,14(3):201-215
[31] 刘家国,周粤湘,卢斌,等. 基于突发事件风险的供应链脆弱性削减机制[J]. 系统工程理论与实践, 2015,35(3):556-566
[32] 胡海青,李浩. 孵化器领导力与孵化网络绩效实证研究[J]. 管理评论, 2016,28(3):164-172
[33] 李锡钦. 结构方程模型:贝叶斯方法[M]. 北京:高等教育出版社, 2011
[34] Shi J. Q., Lee S. Y. Bayesian Sampling-Based Approach for Factor Analysis Models with Continuous and Polytomous Data[J]. British Journal of Mathematical and Statistical Psychology, 1998,51(2):233-252
[35] Zhu H., Sik-Yum L. Statistical Analysis of Nonlinear Factor Analysis Models[J]. British Journal of Mathematical and Statistical Psychology, 1999,52(2):225-242
[36] Song X. Y., Lee S. Y. Bayesian Estimation and Test for Factor Analysis Model with Continuous and Polytomous Data in Several Populations[J]. British Journal of Mathematical and Statistical Psychology, 2001,54(2):237-263
[37] Chai J., Guo J. E., Meng L., et al. Exploring the Core Factors and its Dynamic Effects on Oil Price:An Application on Path Analysis and BVAR-TVP Model[J]. Energy Policy, 2011,39(12):8022-8036
[38] 柴建,张钟毓,李新,等. 中国航空燃油消费分析及预测[J]. 管理评论, 2016,28(1):11-21
[39] Litterman R. B. Forecasting with Bayesian Vector Auto-Regressions:Five Years of Experience[J]. Journal of Busines and Economic Statistics, 1986,4(7):5-15
[40] Todd R. M. Improving Economic Forecasting with Bayesian Vector Autoregression[J]. Quarterly Review, 1984,10(2):107-120
[41] Canova F. You Can Use VARs for Structural Analyses:A Comment to VARs and the Great Moderation[R]. Icrea Universitat Pompeu Fabra Crei & Cepr Working Paper, 2006
[42] Canova F., Ciccarelli M. Estimating Multi-Country VAR Models[J].International Economic Review, 2009,50(3):929-961
[43] Canova F. Bayesian VAR Models[R]. Icrea Universitat Pompeu Fabra Crei & Cepr Working Paper, 2010
[44] 傅志寰,胡思继,姜秀山,等. 中国交通运输中长期节能问题研究[M]. 北京:人民交通出版社, 2011
[45] 张蕴萍. 供给侧改革:中国垄断行业政府规制体制改革的新动力[J]. 理论学刊, 2016,(5):61-66